TY - JOUR
T1 - Mapping of the EORTC QLQ-C30 to EQ-5D-5L index in patients with lymphomas
AU - Xu, Richard Huan
AU - Wong, Eliza Lai Yi
AU - Jin, Jun
AU - Dou, Ying
AU - Dong, Dong
N1 - Funding Information:
No funding supported this study.
Publisher Copyright:
© 2020, Springer-Verlag GmbH Germany, part of Springer Nature.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020/9/22
Y1 - 2020/9/22
N2 - Objective: The objective of this study was to develop algorithms to map the EORTC QLQ-C30 (QLQ-C30) onto EQ-5D-5L in a sample of patients with lymphomas. Methods: An online nationwide survey of patients with lymphoma was carried out in China. Ordinary least squares (OLS), beta-based mixture, adjusted limited dependent variable mixture regression, and a Tobit regression model were used to develop the mapping algorithms. The QLQ-C30 subscales/items, their squared and interaction terms, and respondents’ demographic variables were used as independent variables. The root mean square error (RMSE), mean absolute error (MAE), and R-squared (R2) were estimated based on tenfold cross-validation to assess the predictive ability of the selected models. Results: Data of 2222/4068 respondents who self-completed the online survey were elicited for analyses. The mean EQ-5D-5L index score was 0.81 (SD 0.21, range − 0.81–1.0). 19.98% of respondents reported an index score at 1.0. In total, 72 models were generated based on four regression methods. According to the RMSE, MAE and R2, the OLS model including QLQ-C30 subscales, squared terms, interaction terms, and demographic variables showed the best fit for overall and the Non-Hodgkin’s lymphoma sample; for Hodgkin’s lymphoma, the ALDVMM with 1-component model, including QLQ-C30 subscales, squared terms, interaction terms, and demographic variables, showed a better fit than the other models. Conclusion: The mapping algorithms enable the EQ-5D-5L index scores to be predicted by QLQ-C30 subscale/item scores with good precision in patients living with lymphomas.
AB - Objective: The objective of this study was to develop algorithms to map the EORTC QLQ-C30 (QLQ-C30) onto EQ-5D-5L in a sample of patients with lymphomas. Methods: An online nationwide survey of patients with lymphoma was carried out in China. Ordinary least squares (OLS), beta-based mixture, adjusted limited dependent variable mixture regression, and a Tobit regression model were used to develop the mapping algorithms. The QLQ-C30 subscales/items, their squared and interaction terms, and respondents’ demographic variables were used as independent variables. The root mean square error (RMSE), mean absolute error (MAE), and R-squared (R2) were estimated based on tenfold cross-validation to assess the predictive ability of the selected models. Results: Data of 2222/4068 respondents who self-completed the online survey were elicited for analyses. The mean EQ-5D-5L index score was 0.81 (SD 0.21, range − 0.81–1.0). 19.98% of respondents reported an index score at 1.0. In total, 72 models were generated based on four regression methods. According to the RMSE, MAE and R2, the OLS model including QLQ-C30 subscales, squared terms, interaction terms, and demographic variables showed the best fit for overall and the Non-Hodgkin’s lymphoma sample; for Hodgkin’s lymphoma, the ALDVMM with 1-component model, including QLQ-C30 subscales, squared terms, interaction terms, and demographic variables, showed a better fit than the other models. Conclusion: The mapping algorithms enable the EQ-5D-5L index scores to be predicted by QLQ-C30 subscale/item scores with good precision in patients living with lymphomas.
KW - China
KW - EORTC QLQ-C30
KW - EQ-5D-5L
KW - Lymphoma
KW - Mapping algorithm
UR - http://www.scopus.com/inward/record.url?scp=85091279777&partnerID=8YFLogxK
U2 - 10.1007/s10198-020-01220-w
DO - 10.1007/s10198-020-01220-w
M3 - Journal article
C2 - 32960388
AN - SCOPUS:85091279777
SN - 1618-7598
VL - 21
SP - 1363
EP - 1373
JO - European Journal of Health Economics
JF - European Journal of Health Economics
IS - 9
ER -